An exploration-enhanced hybrid algorithm based on regularity evolution for multi-objective multi-UAV 3-D path planning

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhenzu Bai, Haiyin Zhou, Juhui Wei, Xuanying Zhou, Yida Ning, Jiongqi Wang
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引用次数: 0

Abstract

Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantial challenges within the multi-objective optimization community. Traditional modeling approaches primarily aim to minimize path length and collision risks, often overlooking the need for a quantitative assessment of communication quality among UAVs. This neglect causes an inadequate representation of their true cooperative capabilities. In addition, there is difficulty in achieving an optimal balance between convergence, diversity, and feasibility. Therefore, this study introduces a bi-objective, three-dimensional path planning model specifically designed for UAVs. This model features an objective function that quantitatively evaluates inter-UAV communication quality throughout their flights. To solve this problem, this study proposes the dual-population regularity evolution algorithm (DPREA), which incorporates an auto-switching regularity evolutionary reproduction operator known as autoRE. It conducts extensive experiments across six testing suites and three path-planning simulation cases to validate the effectiveness of DPREA. The simulation results showed that its performance in addressing constrained multi-objective problems is significantly superior or at least comparable to that of state-of-the-art algorithms in most instances.

基于规则演化的探索增强混合算法用于多目标多无人机三维路径规划
路径规划是一项复杂的优化问题,对无人机的安全运行和成功执行任务至关重要。在多目标优化领域,开发涉及多无人机的三维路径规划的目标、约束和决策过程提出了重大挑战。传统的建模方法主要是为了最小化路径长度和碰撞风险,往往忽略了对无人机之间通信质量定量评估的需要。这种忽视导致它们真正的合作能力没有得到充分体现。此外,难以在收敛性、多样性和可行性之间取得最佳平衡。因此,本研究引入了一种针对无人机的双目标三维路径规划模型。该模型具有一个目标函数,可以定量评估无人机在整个飞行过程中的通信质量。为了解决这一问题,本文提出了双种群规则进化算法(DPREA),该算法引入了自动切换规则进化繁殖算子autoore。在六个测试套件和三个路径规划仿真案例中进行了广泛的实验,以验证DPREA的有效性。仿真结果表明,在大多数情况下,该算法在解决约束多目标问题方面的性能明显优于或至少与最先进的算法相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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